Systems and methods for optimizing an online on-demand service

ABSTRACT

Systems and methods for optimizing an online on-demand service are provided. A method may include: obtaining driver information associated with a plurality of historical drivers corresponding to the plurality of historical orders; for each historical driver during a predetermined period of time, determining a plurality of records based on the order information and the driver information according to a decision-making processes, each record includes a driver&#39;s space-time status, a driver&#39;s action, a driver&#39;s revenue, and a driver&#39;s subsequent space-time status; and determining a value function based on the plurality of records of each historical driver according to a reinforcement learning algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2017/116635, filed on Dec. 15, 2017, the contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods foroptimizing an online on-demand service, and in particular, to systemsand methods for optimizing allocations of drivers to incoming orders inan online on-demand service.

BACKGROUND

On-demand transportation services, especially online car hailingservices, have become more and more popular. When a service requester(e.g., a passenger) sends a service request to an online car hailingservice platform, the platform may allocate a service provider (e.g., adriver) for the service requester according to artificial rules thatintend to make the allocations more efficient and profitable. A problemof the existing technology for allocating service providers to servicerequesters, however, is that the artificial rules have their limitationsin adapting to the development of the services and also sometimes lackaccuracy on different business scenarios (e.g., a driver's online andoffline behaviors, carpooling orders, reserving orders, upgradingorders, etc.). Accordingly, it is desirable to provide systems andmethods for optimizing allocation of drivers to incoming orders tooptimize an online on-demand service.

SUMMARY

According to an aspect of the present disclosure, a system may includeat least one storage medium including a set of instructions forallocating orders in an online on-demand service, and at least oneprocessor in communication with the storage medium, when executing theset of instructions, the at least one processor may be directed to:obtain order information associated with a plurality of historicalorders; obtain driver information associated with a plurality ofhistorical drivers corresponding to the plurality of historical orders;for each historical driver during a predetermined period of time,determine a plurality of records based on the order information and thedriver information according to a decision-making processes model; anddetermine a value function based on the plurality of records of eachhistorical driver according to a reinforcement learning algorithm. Insome embodiments, the decision-making processes model is a MarkovDecision Process (MDP) model. In some embodiments, each record includesa driver's space-time status, a driver's action, a driver's revenue, anda driver's subsequent space-time status.

In some embodiments, the reinforcement learning algorithm is atemporal-difference learning algorithm.

In some embodiments, the reinforcement learning algorithm is a dynamicprogramming algorithm.

In some embodiments, for each historical driver during the predeterminedperiod of time, to determine the plurality of records, the at least oneprocessor is further directed to: determine one or more order records,each corresponding to a historical order; and determine one or morespare-time records, each corresponding to a period of idle time notassociated with any historical order.

In some embodiments, for each historical driver during the predeterminedperiod of time, to determine the plurality of records, the at least oneprocessor is further directed to: determine one or more order records,each corresponding to a historical order; and obtain an online timepoint and an offline time point of the historical driver in thepredetermined period of time based on the driver information; determineat least one of spare-time records that corresponds to a period of idletime between the online time point and a time point for accepting afirst historical order; and determine at least another one of spare-timerecords that corresponds to a period of idle time between a time pointfor finishing a last historical order and the offline time point.

In some embodiments, the plurality of records may include at least oneorder record and at least one spare-time record, an order recordcorresponds to a historical order and includes at least one of: adriver's space-time status including a time and a location of thehistorical driver when accepting the historical order, a driver's actionincluding accepting the historical order, a driver's revenue including avalue of the historical order, and a driver's subsequent space-timestatus including a time and a location of the historical driver whenfinishing the historical order; or a spare-time record corresponds to aperiod of idle time not associated with any historical order andincludes at least one of: a driver's space-time status including a timeand a location of the historical driver during the period of idle time,a driver's action including being idle during the period of idle time, adriver's revenue including zero, or a driver's subsequent space-timestatus including a subsequent time and a subsequent location of thehistorical driver at the end of the period of idle time. In someembodiments, the period of idle time is a predetermined duration in theidle time.

In some embodiments, for each historical driver during the predeterminedperiod of time, to determine the plurality of records, the at least oneprocessor is further directed to: identify an order type of a subsequentorder based on the order information; in response to identifying thatthe subsequent order is a reserving order, determine a reference timepoint before a preset buffer time from a start time point of thereserving order; and determine at least one spare-time record thatcorresponds to a period of idle time between a time point for finishingthe historical order and the reference time point.

In some embodiments, for each historical driver during the predeterminedperiod of time, to determine the plurality of records, the at least oneprocessor is further directed to: identify a service type of thehistorical driver; identify an order type of a subsequent order based onthe order information; in response to identifying that the subsequentorder is an upgrade order for the historical driver, identify a starttime point for the historical driver to receive a normal orderassociated with the same service type; and determine at least onespare-time record that corresponds to a period of idle time betweenfinishing the historical order and the start time point.

In some embodiments, for each historical driver during the predeterminedperiod of time, to determine the plurality of records, the at least oneprocessor is further directed to: the order record that corresponds tothe historical order, the historical order is a general carpooling orderthat combines a plurality of carpooling orders having a same route ID,the time and the location of the historical driver are respectively atime and a location of the historical driver when first accepting thegeneral carpooling order, and the value of the historical order is a sumof values of the carpooling orders.

In some embodiments, when executing the set of instructions, the atleast one processor is further directed to: optimize allocations ofdrivers to incoming orders based on the value function.

According to another aspect of the present disclosure, a method forallocating orders in an online on-demand service may be implemented on acomputing device having at least one processor, at least one storagemedium, and a communication platform connected to a network. The methodmay include one or more following operations: obtaining driverinformation associated with a plurality of historical driverscorresponding to the plurality of historical orders; for each historicaldriver during a predetermined period of time, determining a plurality ofrecords based on the order information and the driver informationaccording to a decision-making processes model; and determining a valuefunction based on the plurality of records of each historical driveraccording to a reinforcement learning algorithm. In some embodiments,the decision-making processes model is a Markov Decision Process (MDP)model. In some embodiments, each record includes a driver's space-timestatus, a driver's action, a driver's revenue, and a driver's subsequentspace-time status.

In some embodiments, the reinforcement learning algorithm is atemporal-difference learning algorithm.

In some embodiments, the reinforcement learning algorithm is a dynamicprogramming algorithm.

In some embodiments, for each historical driver during the predeterminedperiod of time, the determining the plurality of records may include oneor more following operations: determining one or more order records,each corresponding to a historical order; and determining one or morespare-time records, each corresponding to a period of idle time notassociated with any historical order.

In some embodiments, for each historical driver during the predeterminedperiod of time, the determining the plurality of records may include oneor more following operations: determining one or more order records,each corresponding to a historical order; and obtaining an online timepoint and an offline time point of the historical driver in thepredetermined period of time based on the driver information;determining at least one of spare-time records that corresponds to aperiod of idle time between the online time point and a time point foraccepting a first historical order; and determining at least another oneof spare-time records that corresponds to a period of idle time betweena time point for finishing a last historical order and the offline timepoint.

In some embodiments, the plurality of records may include at least oneorder record and at least one spare-time record, an order recordcorresponds to a historical order and may include at least one of: adriver's space-time status including a time and a location of thehistorical driver when accepting the historical order, a driver's actionincluding accepting the historical order and servicing (driving) thehistorical order, a driver's revenue including a value of the historicalorder, or a driver's subsequent space-time status including a time and alocation of the historical driver when finishing the historical order;and a spare-time record corresponds to a period of idle time notassociated with any historical order and may include at least one of: adriver's space-time status including a time and a location of thehistorical driver during the period of idle time, a driver's actionincluding being idle during the period of idle time, a driver's revenueincluding zero, or a driver's subsequent space-time status including asubsequent time and a subsequent location of the historical driver atthe end of the period of idle time. In some embodiments, the period ofidle time is a predetermined duration in the idle time.

In some embodiments, for each historical driver during the predeterminedperiod of time, the determining the plurality of records may include oneor more following operations: identifying an order type of a subsequentorder based on the order information; in response to identifying thatthe subsequent order is a reserving order, determining a reference timepoint before a preset buffer time from a start time point of thereserving order; and determining at least one spare-time record thatcorresponds to a period of idle time between a time point for finishingthe historical order and the reference time point.

In some embodiments, for each historical driver during the predeterminedperiod of time, the determining the plurality of records may include oneor more following operations: identifying a service type of thehistorical driver; identifying an order type of a subsequent order basedon the order information; in response to identifying that the subsequentorder is an upgrade order for the historical driver, identifying a starttime point for the historical driver to receive a normal orderassociated with the same service type; and determining at least onespare-time record that corresponds to a period of idle time betweenfinishing the historical order and the start time point.

In some embodiments, for each historical driver during the predeterminedperiod of time, the determining the plurality of records may include oneor more following operations: determining the order record thatcorresponds to the historical order, the historical order is a generalcarpooling order that combines a plurality of carpooling order having asame route ID, the time and the location of the historical driver arerespectively a time and a location of the historical drive when firstaccepting the general carpooling order, and the value of the historicalorder is a sum of values of the carpooling orders.

According to still another aspect of the present disclosure, anon-transitory computer readable medium, comprising at least one set ofinstructions for allocating orders in an online on-demand service, whenexecuted by at least one processor of a computer device, the at leastone set of instructions directs the at least one processor to: obtainorder information associated with a plurality of historical orders;obtain driver information associated with a plurality of historicaldrivers corresponding to the plurality of historical orders; for eachhistorical driver during a predetermined period of time, determine aplurality of records based on the order information and the driverinformation according to a decision making processes (e.g., MDP) model,each record includes a driver's space-time status, a driver's action, adriver's revenue, and a driver's subsequent space-time status; anddetermine a value function based on the plurality of records of eachhistorical driver according to a reinforcement learning algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. The foregoing and other aspects of embodiments of presentdisclosure are made more evident in the following detail description,when read in conjunction with the attached drawing figures.

FIG. 1 is a block diagram of an exemplary system for optimizing anonline on-demand service according to some embodiments of the presentdisclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing engineaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process and/or methodfor optimizing an online on-demand service according to some embodimentsof the present disclosure;

FIG. 6-A is schematic diagrams illustrating exemplary status accordingto some embodiments of the present disclosure;

FIG. 6-B is schematic diagrams illustrating exemplary status accordingto some embodiments of the present disclosure;

FIG. 7 is a schematic diagram illustrating an exemplary process of adynamic programming algorithm according to some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process and/or methodfor obtaining a plurality of records according to some embodiments ofthe present disclosure;

FIG. 9 is a flowchart illustrating an exemplary process and/or methodfor determining a plurality of records according to some embodiments ofthe present disclosure;

FIG. 10 is a flowchart illustrating an exemplary process and/or methodfor determining a plurality of records according to some embodiments ofthe present disclosure; and

FIG. 11 is a flowchart illustrating an exemplary process and/or methodfor determining a plurality of records according to some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure, and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown, but is to beaccorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises,”“comprising,” “includes,” and/or “including” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawing(s), allof which form a part of this specification. It is to be expresslyunderstood, however, that the drawing(s) are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order or simultaneously. Moreover, one ormore other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Moreover, while the system and method in the present disclosure isdescribed primarily in regard to online car hailing services, it shouldalso be understood that this is only one exemplary embodiment. Thesystem or method of the present disclosure may be applied to any otherkind of on-demand service. For example, the system or method of thepresent disclosure may be applied to different transportation systemsincluding land, ocean, aerospace, or the like, or any combinationthereof. The vehicle of the transportation systems may include a taxi, aprivate car, a hitch, a bus, a train, a bullet train, a high speed rail,a subway, a vessel, an aircraft, a spaceship, a hot-air balloon, adriverless vehicle, or the like, or any combination thereof. Thetransportation system may also include any transportation system thatapplies management and/or distribution, for example, a system forsending and/or receiving an express. The application scenarios of thesystem or method of the present disclosure may include a webpage, aplug-in of a browser, a client terminal, a custom system, an internalanalysis system, an artificial intelligence robot, or the like, or anycombination thereof.

The position and/or trace in the present disclosure may be acquired bypositioning technology embedded in a user terminal (e.g., a passengerterminal, a driver terminal). The positioning technology used in thepresent disclosure may include a global positioning system (GPS), aglobal navigation satellite system (GLONASS), a compass navigationsystem (COMPASS), a Galileo positioning system, a quasi-zenith satellitesystem (QZSS), a wireless fidelity (Wi-Fi) positioning technology, orthe like, or any combination thereof. One or more of the abovepositioning technologies may be used interchangeably in the presentdisclosure.

An aspect of the present disclosure relates to online systems andmethods for optimizing an on-demand service. According to the presentdisclosure, the systems and methods may define a plurality of elements(e.g., a status including a time and a location of a historical driver,an action of the historical driver, a revenue that the historical driverobtained under the status, a subsequent status including a subsequenttime and a subsequent location of the historical driver, etc.) accordingto a decision making processes (e.g., MDP) model, and input theplurality of elements into a reinforcement learning algorithm (e.g., atemporal-difference learning algorithm, a dynamic programming algorithm)to obtain a value function. The systems and methods may allocate driversfor passengers based on the value function to optimize allocation ofdrivers to orders in the online on-demand service. According to thepresent disclosure, the systems and methods may define the plurality ofelements according to different business scenarios (e.g., the historicaldrivers' online and offline behaviors, carpooling orders, reservingorders, upgrade orders, etc.), and determine a value function based onthe plurality of elements associated with different business scenarios.

FIG. 1 is a block diagram of an exemplary system 100 for allocatingorders in an online on-demand service according to some embodiments ofthe present disclosure. For example, the system 100 may be an onlinetransportation service platform for transportation services such as carhailing services, chauffeur services, vehicle delivery services,carpooling services, bus services, driver hiring services, and shuttleservices, etc. The system 100 may include a server 110, a passengerterminal 120, a storage 130, a driver terminal 140, a network 150 and aninformation source 160. The server 110 may include a processing engine112.

The server 110 may be configured to process information and/or datarelating to an online on-demand service, for example, allocations ofdrivers to orders. For example, the server 110 may determine a valuefunction for an allocation method, and allocate each order to acorresponding driver terminal 140 according to the allocation method. Insome embodiments, the server 110 may be a single server, or a servergroup. The server group may be centralized, or distributed (e.g., theserver 110 may be a distributed system). In some embodiments, the server110 may be local or remote. For example, the server 110 may accessinformation and/or data stored in the passenger terminal 120, the driverterminal 140 and/or the storage 130 via the network 150. As anotherexample, the server 110 may be directly connected to the passengerterminal 120, the driver terminal 140 and/or the storage 130 to accessstored information and/or data. In some embodiments, the server 110 maybe implemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof. In some embodiments, the server110 may be implemented on a computing device having one or morecomponents illustrated in FIG. 2 in the present disclosure.

In some embodiments, the server 110 may include a processing engine 112.The processing engine 112 may process information and/or data relatingto an online on-demand service, for example, allocations of drivers toorders. For example, the processing engine 112 may determine a valuefunction for an allocation method, and allocate each order to acorresponding driver terminal 140 according to the allocation method. Insome embodiments, the processing engine 112 may include one or moreprocessing engines (e.g., single-core processing engine(s) or multi-coreprocessor(s)). Merely by way of example, the processing engine 112 mayinclude a central processing unit (CPU), an application-specificintegrated circuit (ASIC), an application-specific instruction-setprocessor (ASIP), a graphics processing unit (GPU), a physics processingunit (PPU), a digital signal processor (DSP), a field programmable gatearray (FPGA), a programmable logic device (PLD), a controller, amicrocontroller unit, a reduced instruction-set computer (RISC), amicroprocessor, or the like, or any combination thereof.

In some embodiments, the passenger terminal 120 and/or the driverterminal 140 may be an individual, a tool or other entity directlyrelating to the orders. A passenger may be a service requester. In thepresent disclosure, “service requester”, “passenger terminal” and“passenger” may be used interchangeably. A driver may be a serviceprovider. In the present disclosure, “driver,” “driver terminal”, and“service provider” may be used interchangeably. In some embodiments, thepassenger terminal 120 may include a mobile device 120-1, a tabletcomputer 120-2, a laptop computer 120-3, and a built-in device 120-4 ina motor vehicle, or the like, or any combination thereof. In someembodiments, the mobile device 120-1 may include a smart home device, awearable device, a smart mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, a smartfootgear, a smart glass, a smart helmet, a smart watch, a smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistance (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a GoogleGlass, an Oculus Rift, a HoloLens, a Gear VR, etc. In some embodiments,built-in device in the motor vehicle 120-4 may include an onboardcomputer, an onboard television, etc. In some embodiments, the passengerterminal 120 may be a device with positioning technology for locatingthe position of the user and/or the passenger terminal 120.

In some embodiments, the driver terminal 140 may be similar to, or thesame device as the passenger terminal 120. In some embodiments, thedriver terminal 140 may be a device with positioning technology forlocating the position of the driver and/or the driver terminal 140. Insome embodiments, the passenger terminal 120 and/or the driver terminal140 may communicate with another positioning device to determine theposition of the user, the passenger terminal 120, the driver, and/or thedriver terminal 140. In some embodiments, the passenger terminal 120and/or the driver terminal 140 may transmit positioning information tothe server 110.

The storage 130 may store data and/or instructions related to theorders. In some embodiments, the storage 130 may store dataobtained/acquired from the passenger terminal 120 and/or the driverterminal 140. In some embodiments, the storage 130 may store data and/orinstructions that the server 110 may execute or use to perform exemplarymethods described in the present disclosure. In some embodiments, thestorage device 140 may include a mass storage, a removable storage, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. Exemplary mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (PEROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage 130 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage 130 may be connected to the network 150to communicate with one or more components in the system 100 (e.g., theserver 110, the passenger terminal 120, the driver terminal 140). One ormore components in the system 100 may access the data or instructionsstored in the storage 130 via the network 150. In some embodiments, thestorage 130 may be directly connected to or communicate with one or morecomponents in the system 100 (e.g., the server 110, the passengerterminal 120, the driver terminal 140, etc.). In some embodiments, thestorage 130 may be part of the server 110.

The network 150 may facilitate exchange of information and/or data. Insome embodiments, one or more components in the system 100 (e.g., theserver 110, the passenger terminal 120, the storage 130, and the driverterminal 140) may send and/or receive information and/or data to/fromother component(s) in the system 100 via the network 150. For example,the server 110 may obtain/acquire orders from the passenger terminals120 via the network 150. In some embodiments, the network 150 may be anytype of wired or wireless network, or combination thereof. Merely by wayof example, the network 150 may include a cable network, a wirelinenetwork, an optical fiber network, a tele communications network, anintranet, an Internet, a local area network (LAN), a wide area network(WAN), a wireless local area network (WLAN), a metropolitan area network(MAN), a wide area network (WAN), a public telephone switched network(PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, a global system for mobile communications(GSM) network, a code-division multiple access (CDMA) network, atime-division multiple access (TDMA) network, a general packet radioservice (GPRS) network, an enhanced data rate for GSM evolution (EDGE)network, a wideband code division multiple access (WCDMA) network, ahigh speed downlink packet access (HSDPA) network, a long term evolution(LTE) network, a user datagram protocol (UDP) network, a transmissioncontrol protocol/Internet protocol (TCP/IP) network, a short messageservice (SMS) network, a wireless application protocol (WAP) network, aultra wide band (UWB) network, an infrared ray, or the like, or anycombination thereof. In some embodiments, the system 100 may include oneor more network access points. For example, the system 110 may includewired or wireless network access points such as base stations and/orwireless access points 150-1, 150-2, . . . , through which one or morecomponents of the system 100 may be connected to the network 150 toexchange data and/or information.

The information source 160 may be a source configured to provide otherinformation for the system 100. The information source 160 may providethe system 100 with service information, such as weather conditions,traffic information, information of laws and regulations, news events,life information, life guide information, or the like. The informationsource 160 may be implemented in a single central server, multipleservers connected via a communication link, or multiple personaldevices. When the information source 160 is implemented in multiplepersonal devices, the personal devices can generate content (e.g., asreferred to as a “user-generated content”), for example, by uploadingtext, voice, image, and video to a cloud server. An information sourcemay be generated by the multiple personal devices and the cloud server.

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device 200 on which the server 110,the passenger terminal 120, the storage 130, the driver 140 and/or theinformation source 160 may be implemented according to some embodimentsof the present disclosure. The particular system may use a functionalblock diagram to explain the hardware platform containing one or moreuser interfaces. The computer may be a computer with general or specificfunctions. Both types of the computers may be configured to implementany particular system according to some embodiments of the presentdisclosure. Computing device 200 may be configured to implement anycomponents that perform one or more functions disclosed in the presentdisclosure. For example, the computing device 200 may implement anycomponent of the system 100 as described herein. In FIGS. 1-2, only onesuch computer device is shown purely for convenience purposes. One ofordinary skill in the art would understand at the time of filing of thisapplication that the computer functions relating to the on-demandservice as described herein may be implemented in a distributed fashionon a number of similar platforms, to distribute the processing load.

The computing device 200, for example, may include COM ports 250connected to and from a network connected thereto to facilitate datacommunications. The computing device 200 may also include a processor(e.g., the processor 220), in the form of one or more processors (e.g.,logic circuits), for executing program instructions. For example, theprocessor may include interface circuits and processing circuitstherein. The interface circuits may be configured to receive electronicsignals from a bus 210, wherein the electronic signals encode structureddata and/or instructions for the processing circuits to process. Theprocessing circuits may conduct logic calculations, and then determine aconclusion, a result, and/or an instruction encoded as electronicsignals. Then the interface circuits may send out the electronic signalsfrom the processing circuits via the bus 210.

The exemplary computing device may include the internal communicationbus 210, program storage and data storage of different forms including,for example, a disk 270, and a read only memory (ROM) 230, or a randomaccess memory (RAM) 240, for various data files to be processed and/ortransmitted by the computing device. The exemplary computing device mayalso include program instructions stored in the ROM 230, RAM 240, and/orother type of non-transitory storage medium to be executed by theprocessor 220. The methods and/or processes of the present disclosuremay be implemented as the program instructions. The computing device 200also includes an I/O component 260, supporting input/output between thecomputer and other components. The computing device 200 may also receiveprogramming and data via network communications.

Merely for illustration, only one CPU and/or processor is illustrated inFIG. 2. Multiple CPUs and/or processors are also contemplated; thusoperations and/or method steps performed by one CPU and/or processor asdescribed in the present disclosure may also be jointly or separatelyperformed by the multiple CPUs and/or processors. For example, if in thepresent disclosure the CPU and/or processor of the computing device 200executes both step A and step B, it should be understood that step A andstep B may also be performed by two different CPUs and/or processorsjointly or separately in the computing device 200 (e.g., the firstprocessor executes step A and the second processor executes step B, orthe first and second processors jointly execute steps A and B).

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which thepassenger terminal 120 or the provider terminal 140 may be implementedaccording to some embodiments of the present disclosure. As illustratedin FIG. 3, the mobile device 300 may include a communication unit 310, adisplay 320, a graphic processing unit (GPU) 330, a central processingunit (CPU) 340, an I/O 350, a memory 360, and a storage 390. The CPU 340may include interface circuits and processing circuits similar to theprocessor 220. In some embodiments, any other suitable component,including but not limited to a system bus or a controller (not shown),may also be included in the mobile device 300. In some embodiments, amobile operating system 370 (e.g., iOS™, ANDROID™, Windows Phone™, etc.)and one or more applications 380 may be loaded into the memory 360 fromthe storage 390 in order to be executed by the CPU 340. The applications380 may include a browser or any other suitable mobile apps forreceiving and rendering information relating to a service request orother information from the location based service providing system onthe mobile device 300. User interactions with the information stream maybe achieved via the I/O devices 350 and provided to the processingengine 112 and/or other components of the system 100 via the network120.

In order to implement various modules, units and their functionsdescribed above, a computer hardware platform may be used as hardwareplatforms of one or more elements (e.g., a module of the sever 110described in FIG. 2). Since these hardware elements, operating systems,and program languages are common, it may be assumed that persons skilledin the art may be familiar with these techniques and they may be able toprovide information required in the route planning according to thetechniques described in the present disclosure. A computer with userinterface may be used as a personal computer (PC), or other types ofworkstations or terminal devices. After being properly programmed, acomputer with user interface may be used as a server. It may beconsidered that those skilled in the art may also be familiar with suchstructures, programs, or general operations of this type of computerdevice. Thus, extra explanations are not described for the figures.

FIG. 4 is a block diagram illustrating an exemplary processing engine112 according to some embodiments of the present invention. In someembodiments, the processing engine 112 may include a communicationmodule 410, a record determination module 420, a value functiondetermination module 430, and an allocation optimization module 440.Each, part, or all of the modules may be hardware circuits of all orpart of the processing engine 112. Each, part, or all of the modules mayalso be implemented as an application or a set of instructions that canbe read and executed by the processing engine. Further, the modules maybe any combination of the hardware circuits and theapplication/instructions. For example, the modules may be the part ofthe processing engine 112 when the processing engine is executing theapplication/set of instructions.

The communication module 410 may be configured to obtain informationassociated with the online on-demand service. For example, thecommunication module 410 may be configured to obtain order informationassociated with one or a plurality of historical orders. As anotherexample, the communication module 410 may be configured to obtain driverinformation associated with one or a plurality of historical driverscorresponding to the one or plurality of historical orders. Forillustration purposes, the descriptions would refer to “a plurality ofhistorical orders” and “a plurality of historical drivers” as examples.It should be noted that in some embodiments, order informationassociated with only one historical driver or driver informationassociated with only one historical order is obtained.

The term “order information” in the present disclosure may refer to acombination of information and/or features associated with one or aplurality of historical orders. For example, the order information of ahistorical order may include a historical order number, a historicalorder start location, a historical order destination, a historical orderstart time, a historical order end time, a passenger that initiated thehistorical order, a historical driver that completed the historicalorder, a driver type of the historical driver, an order type of thehistorical order, an order value of the historical order, or the like,or any combination thereof. The term “historical order” in the presentdisclosure may refer to an order that was made by a service requesterthrough the online on-demand service platform and was completed by aservice provider in the past.

The term “driver information” in the present disclosure may refer to acombination of information and/or features associated with one or aplurality of historical drivers. For example, the driver information ofa historical driver may include at least one space-time status(including a location and a corresponding time) of the historicaldriver, at least one action that the historical driver took (e.g.,accepting an historical order, being idle during a period of idle time,etc.), at least one subsequent space-time status (including a subsequentlocation and a corresponding subsequent time) of the historical driver,a driver identification (e.g., a registration number of the driver), avehicle type of a vehicle owned by the driver, a vehicle identification(e.g., a plate number of the vehicle), a driver profile (e.g., a servicescore of the driver), or the like, or any combination thereof. The term“historical driver” in the present disclosure may refer to a serviceprovider who registered on the platform and provided service(s) in thepast.

The record determination module 420 may be configured to determine aplurality records. In some embodiments, for each historical driverduring a predetermined period of time, the record determination module420 may be configured to determine a plurality of records based on theorder information and driver information obtained from the communicationmodule 410 according to a decision-making processes model, such as butnot limited to a Markov Decision Process (MDP) model. For example, therecord determination module 420 may be configured to determine one ormore order records and one or more spare-time records. Each order recordmay correspond to a historical order, and each spare-time record maycorrespond to a period of idle time not associated with any historicalorder. As another example, the record determination module 420 may beconfigured to determine at least one spare-time record that correspondsto a period of idle time based on an online time point and an offlinetime point of the historical driver. As still another example, therecord determination module 420 may be configured to identify that asubsequent order is a reserving order (or an upgrade order), anddetermine at least one spare-time record that corresponds to a period ofidle time based on the reserving order (or the upgrade order). As stillanother example, the record determination module 420 may be configuredto determine an order record based on a general carpooling order thatcombines a plurality of carpooling orders having a same route ID.

In some embodiments, the plurality of records may include at least oneorder record and/or at least one spare-time record. In some embodiments,an order record may correspond to a historical order that was acceptedand completed by a historical driver. The order record may include oneor more elements associated with the historical order and the historicaldriver corresponding to the historical order. For example, the orderrecord may include a driver's space-time status including a time and alocation of the historical driver when accepting the historical order, adriver's action including accepting the historical order and servicing(driving) the historical order, a driver's revenue including the valueof the historical order, a driver's subsequent space-time statusincluding a time and a location of the historical driver when finishingthe historical order, or the like, or any combination thereof. In someembodiments, a spare-time record may correspond a period of idle timenot associated with any historical order. The order record may includeat least one element associated with a period of idle time and ahistorical driver corresponding to the period of idle time. For example,the spare-time record may include a driver's space-time status includinga time and a location of the historical driver during the period of idletime, a driver's action including being idle during the period of idletime, a driver's revenue including zero, a driver's subsequentspace-time status including a subsequent time and a subsequent locationof the historical driver at the end of the period of idle time, or thelike, or any combination thereof.

The value function determination module 430 may be configured todetermine a value function. For example, value function determinationmodule 430 may be configured to determine a value function based on thedetermined plurality of records of each historical driver according to areinforcement learning algorithm. A more detailed description of theactions of the value function determination module 430 is provided inthe descriptions associated with FIG. 5.

The allocation optimization module 440 may be configured to optimizeallocations of drivers to incoming orders in the online on-demandservice. For example, the allocation optimization module 440 may beconfigured to optimize allocations of drivers to incoming orders basedon the value function determined by the value function determinationmodule 430. A more detailed description of the actions of the allocationoptimization module 440 is provided in the descriptions associated withFIG. 5.

The modules in the processing engine 112 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth™, a ZigBee™, a Near Field Communication(NFC), or the like, or any combination thereof. Two or more of themodules may be combined as a single module, and any one of the modulesmay be divided into two or more units. For example, the recorddetermination module 420 may be integrated into the value functiondetermination module 440 as a single module that may both determine theplurality of records and the value function. As another example, thecommunication module 410 may be divided into two units: an orderinformation obtaining unit and a driver information obtaining unit,which work together to implement the functions of the communicationmodule 420, respectively. It should be noted that FIG. 4 onlyillustrates four modules in the processing engine 112, the processingengine 112 may further include one or more other modules. For example,the processing engine may further include an incoming order obtainingmodule for obtaining at least one incoming order, and an orderallocation module for allocating a driver for each of the at least oneincoming order.

FIG. 5 is a flowchart of an exemplary process and/or method 500 foroptimizing an online on-demand service according to some embodiments ofthe present disclose. In some embodiments, one or more steps in theprocess 500 may be implemented in the system 100 illustrated in FIG. 1.For example, one or more steps in the process 500 may be stored in thestorage (e.g., the storage 130, the ROM 230, the RAM 240, etc.) as aform of instructions, and invoked and/or executed by the server 110(e.g., the processing engine 112 in the server 110, or the processor 220of the processing engine 112 in the server 110).

In 510, the processor 220 (or the communication module 410) may obtainorder information associated with a plurality of historical orders.

The term “historical order” in the present disclosure may refer to anorder that requested by a service requester and has completed by aservice provider on the online on-demand service platform in the past.The term “order information” in the present disclosure may refer to acombination of information and/or feature associated with one or aplurality of historical orders. For example, the order informationassociated with a historical order may include a historical ordernumber, a historical order start location, a historical orderdestination, a historical order start time, a historical order end time,a passenger that initiated the historical order, a historical driverthat completed the historical order, a driver type of the historicaldriver, an order type of the historical order, an order value of thehistorical order, or the like, or any combination thereof.

In some embodiments, once an order is completed, the order is referredto as a historical order. The passenger terminal 120 that requested thehistorical order and/or the driver terminal 140 that completed thehistorical order may send the order information to a storage medium(e.g., the storage 130, the ROM 230, the RAM 240, etc.) of the system100 via the network 150. The processor 220 may obtain the orderinformation from the storage medium of the system 100.

In some embodiments, the plurality of historical orders may include atleast one historical order that was completed in a predetermined periodof time (e.g. 1 day, 2 days, 3 days, 5 days, 7 days, 1 month, 3 months,6 month, 1 year, 2 years, 3 years, 5 years, etc.) in the past. Forexample, the processor 220 may obtain order information associated withat least one historical order that was completed in the past year, inthe past month, in the past day, in the past hour, or the like, or anycombination thereof. In some embodiments, the plurality of historicalorders may include a predetermined number of historical orders (e.g., 1million orders, 100,000 orders, 10,000 orders, 1,000 order, 100 orders,50 orders, 10 orders, 5 orders, 1 order, etc.) that was completed in arecent period of time in the past.

In 520, the processor 220 (or the communication module 410) may obtaindriver information associated with a plurality of historical driverscorresponding to the plurality of historical orders.

The term “historical driver” in the present disclosure may refer to aservice provider who registered on the platform and provided service(s)in the past. In some embodiments, a historical driver may correspond toat least one historical order, and a historical order may correspond toonly one historical order. For example, a historical driver may havecompleted at least one historical order to provide service(s) in thepast, and a historical order may be completed by only one historicaldriver.

The term “driver information” in the present disclosure may refer to acombination of information and/or features associated with one or aplurality of historical drivers. For example, the driver information ofa historical driver may include at least one space-time status(including a location and a corresponding time) of the historicaldriver, at least one action that the historical driver took (e.g.,accepting an historical order, being idle during a period of idle time,etc.), at least one subsequent space-time status (including a subsequentlocation and a corresponding subsequent time) of the historical driver,a driver identification (e.g., a registration number of the driver), avehicle type of a vehicle owned by the driver, a vehicle identification(e.g., a plate number of the vehicle), a driver profile (e.g., a servicescore of the driver), or the like, or any combination thereof.

In some embodiments, once a driver registers on the online on-demandplatform and/or completes an order, the driver terminal 140 associatedwith the driver may send the corresponding driver information to astorage medium (e.g., the storage 130, the ROM 230, the RAM 240, etc.)of the system 100 via the network 150. The processor 220 may obtain thedriver information from the storage medium of the system 100.

In 530, for each historical driver during a predetermined period oftime, the processor 220 (or the record determination module 420) maydetermine a plurality of records based on the order information and thedriver information according to a decision-making processes, such as butnot limited to a Markov Decision Process (MDP) model, which is used asan example for illustration purposes. In some embodiments, the processor220 may go through every historical driver in the plurality ofhistorical drivers to obtain a large number of orders according to theMDP model.

In some embodiments, the predetermined period of time may refer to apredetermined period of time (e.g. 1 day, 2 days, 3 days, 5 days, 7days, 1 month, 3 months, 6 month, 1 year, 2 years, 3 years, 5 years,etc.) in the past, and be determined according to different applicationscenarios. For example, the predetermined period of time may be acertain period of time predetermined by the system 100, such as the pastyear, the past month, the past day, the past hour, etc. In someembodiments, the processor 220 may extract at least one historical orderand at least one corresponding historical driver during thepredetermined period of time based on the order time (e.g., the orderstart time, the order end time, etc.). For example, the processor mayobtain order information associated with the at least one historicalorder and driver information associated with the corresponding at leastone historical driver to determine the plurality of records for eachhistorical driver, respectively.

In some embodiments, for a historical driver, the processor 220 mayinput the order information and the corresponding driver informationduring the predetermined period of time into the MDP model. The MDPmodel may define the historical driver as an agent, and each record mayinclude four elements <S, A, R, P>. In some embodiments, the fourelements may include a driver's space-time status (S), a driver's action(A), a driver's revenue (R), a driver's subsequent space-time status(P), or the like, or any combination thereof. For example, in the MDPmodel, at each time step, a process (here refers to as providingservices on the online on-demand platform) is in a state S, and thedecision maker (here refers to as the historical driver) may choose anaction A that is available in the status S. The process responds at thenext time by randomly moving into the subsequent status P, and givingthe decision maker a corresponding reward R. In some embodiments, theoutput of the MDP model may include a value function (V(S)). In someembodiments, the value function (V(S)) may refer to an expectation valueof an accumulated revenue that the historical driver may obtain underthe driver's space-time status (S).

In some embodiments, the driver's space-time status (S) may refer to asituation that the corresponding historical driver is located at aparticular time. For example, the driver's space-time status may includea combination of a time and a location. The time may be a time point ora period of time when the historical driver was at the location, and thelocation may be a position or an area that the historical driver waslocated at the time point or the period of time. In some embodiments,the location may include a location name, an area name, coordinates ofthe location, or the like, or any combination thereof.

In some embodiments, the driver's action (A) may refer to an action thatthe corresponding historical driver took under the driver's space-timestatus. For example, the driver's action may include accepting ahistorical order, rejecting a historical order, working (driving) undera historical order, finishing a historical order, being idle (neitheraccepting any historical order nor being in service of any historicalorder), being offline, or the like, or any combination thereof.

In some embodiments, the driver's revenue (R) may refer to an incomethat the corresponding historical driver brings in as a result of thedriver's action. For example, if a historical driver accepted ahistorical order, the historical driver would bring in an order value ofthe historical order. Accordingly, the driver's revenue may be the ordervalue of the historical order. As another example, if the historicaldriver rejected the historical order (or is idle, is offline), thehistorical driver would bring in nothing. Accordingly, the driver'srevenue may be zero.

In some embodiments, the driver's subsequent space-time status (P) mayrefer to a situation that the corresponding historical driver is locatedat a subsequent time of the particular time corresponding to a previousstatus. For example, the driver's subsequent space-time status mayinclude a combination of a subsequent time and a subsequent location.The subsequent time may be a time point or a period of time when thehistorical driver was at the subsequent location, and the subsequentlocation may be a position or an area that the historical driver waslocated at the subsequent time point or the subsequent period of time.In some embodiments, the subsequent location may include a subsequentlocation name, a subsequent area name, coordinates of the subsequentlocation, or the like, or any combination thereof. In some embodiments,the subsequent location may be the same as the location in the driver'sspace-time status. For example, the historical driver may stay at a sameplace without moving. In some embodiments, the subsequent location maybe different from the location in the driver's space-time status. Forexample, the historical driver may move to another place at thesubsequent time.

In some embodiments, the plurality of records may include differenttypes of records according to different status and/or actions of thehistorical drivers. For example, the plurality of records may include atleast one order record and at least one spare-time record. In someembodiments, an order record may refer to a record that corresponds to ahistorical order in response that the corresponding historical driveraccepted the historical order. For example, an order record may includea driver's space-time status including a time and a location of thehistorical driver when accepting the historical order, a driver's actionincluding accepting the historical order and servicing (driving) thehistorical order, a driver's revenue including a value of the historicalorder, a driver's subsequent space-time status including a time and alocation of the historical driver when finishing the historical order,or the like, or any combination thereof. In some embodiments, aspare-time order may refer to an order that corresponds to a period ofidle time not associated with any historical order, wherein thecorresponding historical driver was idle without accepting anyhistorical order or providing service during any historical order time.For example, a spare-time order may include a driver's space-time statusincluding a time and a location of the historical driver during theperiod of idle time, a driver's action including being idle during theperiod of idle time, a driver's revenue including zero, a driver'ssubsequent space-time status including a subsequent time and asubsequent location of the historical driver at the end of the period ofidle time, or the like, or any combination thereof. In some embodiments,the period of idle time may be a predetermined duration in the idletime. In some embodiments, the obtaining of the plurality of ordersincluding one or more order records and/or one or more spare-time recordmay be described in connection with FIGS. 8-11 and the descriptionsthereof in the present disclosure.

In 540, the processor 220 (or the value function determination module430) may determine a value function based on the plurality of orders ofeach historical driver according to a reinforcement learning algorithm.

In some embodiments, the value function may refer to a function thatrepresents an expectation value of an accumulated revenue that ahistorical driver may obtain under a driver's space-time status. In someembodiments, the value function may include an algorithm, a formula, amethod, a process, or the like, for determining the expectation value ofan accumulated revenue that a historical driver would bring in.

In some embodiments, the reinforcement learning algorithm may include atemporal-difference (TD) learning algorithm, a dynamic programmingalgorithm, a Monte Carlo (MC) learning algorithm, a SARSA algorithm, aQ-learning algorithm, or the like, or any combination thereof. Forillustration purpose, the TD learning algorithm and dynamic programmingalgorithm are described herein as examples to determine a valuefunction.

In some embodiments, the TD learning algorithm may simulate (or gothrough) every or several steps during a series of actions. The TDlearning algorithm may estimate a value of a previous status before thenext status according to a brought-in revenue and the value of the nextstatus. For example, the processor 220 may input the plurality of ordersof each historical driver (e.g., <S, A, R, P>) into Formula (1):V(S _(t))←V(S _(t))+α[R _(t+1) +γV(S _(t+1))−V(S _(t))]  (1),wherein t denotes a time point of taking an action, t+1 denotes asubsequent time point of t, S_(t) denotes a status before taking theaction, R_(t+1) denotes a revenue that a historical driver would bringin at t+1, V(S_(t)) and V(S_(t+1)) respectively denotes a value functionof the status S_(t) and the status S_(t+1), γ denotes a decay factorbetween 0 and 1, and a denotes a learning rate.

In some embodiments, the processor 220 may input the plurality of ordersof each historical driver into the Formula (1) until a convergence ofthe Formula (1). Then the processor 220 may determine the value function(V(s)). It should be noted that the Formula (1) is only one step of TDlearning algorithm, other formulas that include a plurality of step maybe used for determining the value function.

FIG. 6-A and FIG. 6-B are schematic diagrams illustrating exemplarystatus according to some embodiments of the present disclosure. As shownin FIG. 6-A, a historical driver is at location X at time point T₀ (alsoreferred to as status S₀) and at location X at a sequent time point T₁(also referred to as status S₁), which, in combination with otherinformation, indicates that during the period of time from T₀ to T₁, thehistorical driver is idle and not associated with any historical order.The processor 220 may input the corresponding records associated withstatus S₀ and S₁ into the Formula (1) to determine the value function ofS₀ as Formula (2):V(S ₀)←V(S _(t))+α[R _(t+1) +γV(S _(t+1))−V(S ₀)]  (2),wherein a revenue that the historical driver brought in at t+1 is 0.

As shown in FIG. 6-B, a historical driver is at location X at time pointT₀ (also referred to as status S₀) and at location Y at a sequent timepoint T₂ (also referred to as status S₂), which, in combination withother information, indicates that during the period of time from T₀ toT₂, the historical driver accepted a historical order. The processor 220may input the corresponding records associated with status S₀ and S₂into the Formula (3) to determine the value function of S₀:V(S ₀)←V(S ₀)+α[R+γ ³ V(S ₂)−V(S ₀)]  (3),wherein a revenue that the historical driver brings in at t+2 is R.

FIG. 7 is a schematic diagram illustrating an exemplary process of adynamic programming algorithm according to some embodiments of thepresent disclose. As shown in FIG. 7, the dynamic programming algorithmmay start at a status St, and go through every possible status todetermine the value function.

In some embodiments, the dynamic programming algorithm may turn amulti-stage process into a single-stage process. The dynamic programmingalgorithm may solve the value function one by one according torelationships of each stage. For example, the dynamic programmingalgorithm may simulate status transition probability based on theplurality of orders of each historical driver. For example, theprocessor 220 may input the plurality of orders of each historicaldriver into Formula (4) to determine a value function of any location atevery time point from back forward on the timeline:V _(k+1)(S)=E _(π)[R _(t+1) +γV _(k)(S _(t+1))|S _(t) =S]  (4),wherein t denotes a time point of taking an action, t+1 denotes asubsequent time point of t, S_(t) denotes a status before taking theaction, R_(t+1) denotes a revenue that a historical driver brings in att+1, V(S_(t)) and V(S_(t+1)) respectively denotes a value function ofthe status S_(t) and the status S_(t+1), γ denotes a decay factorbetween 0 and 1, and E_(π) denotes a mathematical expectation.

In 550, the processor 220 (or the allocation optimization module 440)may optimize allocations of drivers to incoming orders based on thevalue function.

In some embodiments, the incoming orders may refer to at least one orderthat the system 100 obtains at a current time. The processor 220 maydetermine an allocation strategy and/or method based on the valuefunction, and allocate each order in the incoming orders to a driverbased on the allocation strategy and/or method. For example, theprocessor 220 may determine an estimated value that a driver may bringin if he/she is allocated each order of the incoming orders, andallocate an order corresponding to a higher estimated value to a certaindriver (e.g., a driver with a higher service score).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional steps (e.g., a storing step, a preprocessing step)may be added elsewhere in the exemplary process/method 500. As anotherexample, all the steps in the exemplary process/method 500 may beimplemented in a computer-readable medium including a set ofinstructions. The instructions may be transmitted in a form ofelectronic current or electrical signals.

FIG. 8 is a flowchart of an exemplary process and/or method 800 forobtaining a plurality of records according to some embodiments of thepresent disclose. In some embodiments, one or more steps in the process800 may be implemented in the system 100 illustrated in FIG. 1. Forexample, one or more steps in the process 800 may be stored in thestorage 130 and/or the storage (e.g., the ROM 230, the RAM 240, etc.) asa form of instructions, and invoked and/or executed by the server 110(e.g., the processing engine 112 in the server 110, or the processor 220of the processing engine 112 in the server 110).

In some embodiments, the processor 220 may implement the process and/ormethod 800 for obtaining the plurality of records in the descriptions ofFIG. 5. For example, the processor 220 may implement the process and/ormethod 800 for demining one or more order records and/or one or morespare-time records according to a decision-making processes model (e.g.,a MDP model), so as to obtain the plurality of records including one ormore order records and/or one or more spare-time records. In someembodiments, the one or more order records and/or one or more spare-timerecords determined in the process and/or method 800 may be used todetermine a value function according to a reinforcement learningalgorithm in the descriptions of FIG. 5.

In 810, the processor 220 (or the record determination module 420) maydetermine one or more order records. In some embodiments, each orderrecord may correspond to a historical order. In some embodiments, duringthe predetermined period of time, for each driver, the processor maydetermine an order record corresponding to each historical order.

In some embodiments, an order record may refer to a record thatcorresponds to a historical order in which that the correspondinghistorical driver accepted the historical order. In some embodiments, anorder record may include a driver's space-time status including a timeand a location of the historical driver when accepting the historicalorder, a driver's action including accepting the historical order andservicing (driving) the historical order, a driver's revenue including avalue of the historical order, a driver's subsequent space-time statusincluding a time and a location of the historical driver when finishingthe historical order, or the like, or any combination thereof.

In some embodiments, the historical order is a general carpooling order.The general carpooling order may refer to an order that combines aplurality of carpooling orders having a same route ID, before or afterthe routes of the carpooling orders are processed and optimized. Eachcarpooling order may correspond to a service request. In someembodiments, the route ID may refer to an identification of a drivingroute. The processor 220 may assign a route ID for each carpoolingorder. For example, the carpooling orders that can be finished by a samedriver at a same period of time, and have at least a part of samedriving route may be assigned to a same route ID. The carpooling ordershaving the same route ID may be included in a general carpooling order,which is assigned to a driver. In some embodiments, the processor 220may generate an order record that corresponds to the general carpoolingorder. The order record may include a driver's space-time statusincluding a time and a location of the historical driver when firstaccepting the general carpooling order, a driver's action includingaccepting the general carpooling order and servicing (driving) thegeneral carpooling order, a driver's revenue including a sum of valuesof the carpooling orders, a driver's subsequent space-time statusincluding a time and a location of the historical driver when lastfinishing the general carpooling order, or the like, or any combinationthereof. In some embodiments, the time and the location of thehistorical driver when first accepting the general carpooling order mayrefer to a time and a location of a carpooling order from the carpoolingorders with the same route ID that started earliest among the carpoolingorders. The sum of values of the carpooling orders may refer to a sum oforder values of the carpooling orders with the same route ID. In someembodiments, the value of each carpooling orders may refer to a valuethat is displayed to a user and refer to a value that the user may payfor each carpooling order. The time and the location of the historicaldriver when finishing the general carpooling order may refer to a timeand a location of a carpooling order from the carpooling orders with thesame route ID that ended latest among the carpooling orders.

In 820, the processor 220 (or the record determination module 420) maydetermine one or more spare-time records. In some embodiments, eachspare-time order may correspond to a period of idle time not associatedwith any historical order.

In some embodiments, the period of idle time may be determined accordingto different application scenarios. For example, the period of idle timemay be different according to different time, different areas, or thelike, or any combination thereof. As another example, the period of idletime may be a certain period of time determined by the system 100, suchas 10 minutes, 20 minutes, 45 minutes, etc. Merely by way of example,the processor 220 may generate a spare-time record every 10 minutes. Ahistorical driver D was idle from 8:00 to 8:20, and accepted ahistorical order at 8:23. The processor may generate two spare-timerecords between 8:00 to 8:30: Record 1 from 8:00 to 8:10, started fromLocation A, to 8:10 to 8:20, ended at Location A, Revenue is 0; andRecord 2 from 8:10 to 8:20, started from Location A, to 8:20 to 8:30,ended at Location A, Revenue is 0.

In some embodiments, a spare-time order may refer to a record thatcorresponds to a period of idle time not associated with any historicalorder in response that the corresponding historical driver was idlewithout accepting any historical order nor servicing any historicalorder. In some embodiments, a spare-time order may include a driver'sspace-time status including a time and a location of the historicaldriver during the period of idle time, a driver's action including beingidle during the period of idle time, a driver's revenue including zero,a driver's subsequent space-time status including a subsequent time anda subsequent location of the historical driver at the end of the periodof idle time, or the like, or any combination thereof. In someembodiments, the period of idle time may be a predetermined duration inthe idle time.

In some embodiments, the time and locations in the order record (or thespare-time record) may be quantized values. For example, the time may bequantized as intervals having a certain number of minutes, such as 1minute, 10 minutes, 30 minutes, 300 minutes, etc. As another example,the processor 220 may divide an area into a plurality of rectangulargrids or grids having other shapes. The locations may be quantized as agranularity of the rectangular grids. For example, a rectangular gridmay be 1 kilometer*1 kilometer or 700 m*700 m, the location may be 1rectangular grid, 10 rectangular grids, 100 rectangular grids, etc.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional steps (e.g., a storing step, a preprocessing step)may be added elsewhere in the exemplary process/method 800. As anotherexample, all the steps in the exemplary process/method 800 may beimplemented in a computer-readable medium including a set ofinstructions. The instructions may be transmitted in a form ofelectronic current or electrical signals.

FIG. 9 is a flowchart of an exemplary process and/or method 900 fordetermining a plurality of records according to some embodiments of thepresent disclose. In some embodiments, one or more steps in the process900 may be implemented in the system 100 illustrated in FIG. 1. Forexample, one or more steps in the process 900 may be stored in thestorage 130 and/or the storage (e.g., the ROM 230, the RAM 240, etc.) asa form of instructions, and invoked and/or executed by the server 110(e.g., the processing engine 112 in the server 110, or the processor 220of the processing engine 112 in the server 110).

In some embodiments, the processor 220 may implement the process and/ormethod 900 for obtaining the plurality of records in the descriptions ofFIG. 5. For example, the processor 220 may implement the process and/ormethod 900 for demining one or more order records and/or one or morespare-time records according to a decision-making processes model (e.g.,a MDP model), so as to obtain the plurality of records including one ormore order records and/or one or more spare-time records. In someembodiments, the one or more order records and/or one or more spare-timerecords determined in the process and/or method 900 may be used todetermine a value function according to a reinforcement learningalgorithm in the descriptions of FIG. 5.

In 910, the processor 220 (or the record determination module 420) maydetermine one or more order records. In some embodiments, each orderrecord may correspond to a historical order. The order record may bedescribed in connection with FIG. 8 and the description thereof in thepresent disclosure.

In 920, the processor 220 (or the record determination module 420) mayobtain an online time point and an offline time point of the historicaldriver in the predetermined period of time based on the driverinformation.

In some embodiments, the processor 220 may extract an online time pointand an offline time point of the historical driver in the predeterminedperiod of time from the driver information. The predetermined period oftime may be determined according to different application scenarios. Forexample, the predetermined period may be an hour, several hours, oneday, a week, a month, a year, etc.

In some embodiments, the online time point may refer to a time pointthat the historical driver was connected to the online platform, andprepared to be allocated or accept an order, or was serving a historicalorder. For example, the online time point may be a time point that thehistorical driver logged in an application installed in the driver'smobile device via the network 150. As still another example, the onlinetime point may be a time point that the historical driver turned on aswitch in the application to start a service function via the network150.

In some embodiments, the offline time point may refer to a time pointthat the historical driver disconnected from the online platform, andwas unavailable to be allocated or accept an order. For example, theoffline time point may be a time point that the historical driver loggedout an application installed in the driver's mobile device via thenetwork 150. As still another example, the offline time point may be atime point that the historical driver turned off a switch in theapplication to close a service function via the network 150.

In 930, the processor 220 (or the record determination module 420) maydetermine at least one of spare-time records that corresponds to aperiod of idle time between the online time point and a time point foraccepting a first historical order. In some embodiments, such one of thespare-time record corresponds to a time period after the driver loggedon and before the driver started the first order.

In some embodiments, the processor 220 may first obtain the time pointfor accepting the first historical order during the predetermined periodof time from the order information and the corresponding driverinformation. During the online time point and the time point foraccepting the first historical order, the processor 220 may generate aspare-time record in every period of idle time. For example, ahistorical driver came online at 7:55 of a day, and at 8:23, he/sheaccepted (or was allocated to) a historical order. The processor 220 maygenerate two spare-time records between 8:00 to 8:30: Record 1: “from8:00 to 8:10, started from Location A” (a driver's space-time status(S)), “to 8:10 to 8:20, ended at Location A” (a driver's subsequentspace-time status (P)), “Revenue is 0” (a driver's revenue (R)); andRecord 2: “from 8:10 to 8:20, started from Location A” (a driver'sspace-time status (S)), “to 8:20 to 8:30, ended at Location A” (adriver's subsequent space-time status (P)), Revenue is 0 (a driver'srevenue (R)).

In 940, the processor 220 (or the record determination module 420) maydetermine at least another one of spare-time records that corresponds toa period of idle time between a time point for finishing a lasthistorical order and the offline time point. In some embodiments, suchanother one of the spare-time records corresponds to a time period afterthe driver finished the last order and before the driver logged out.

In some embodiments, the processor 220 may first obtain the time pointfor finishing the last historical order during the predetermined periodof time from the order information and the corresponding driverinformation. Between the time point for finishing the last historicalorder and the offline time point, the processor 220 may generate aspare-time record in every period of idle time. For example, ahistorical driver accepted (or was allocated to) a last historical orderin a day at 17:35, and went offline at 18:01. The processor 220 maygenerate two spare-time records between 17:30 to 18:00: Record 1: “from17:30 to 17:40, started from Location C” (a driver's space-time status(S)), “to 17:40 to 17:50, ended at Location V” (a driver's subsequentspace-time status (P)), “Revenue is 0” (a driver's revenue (R)); andRecord 2: “from 17:40 to 17:50, started from Location C” (a driver'sspace-time status (S)), “to 17:50 to 18:00, ended at Location C” (adriver's subsequent space-time status (P)), “Revenue is 0” (a driver'srevenue (R)).

In some embodiments, the processor 220 may not determine (or generate)any spare-time record during the period of time before the online timepoint or the period of time after the time point for finishing the lasthistorical order. In certain embodiments, the lack of such records mayreflect the promptness of the historical driver in getting the firsthistorical order after logging on or logging off after the lasthistorical order.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional steps (e.g., a storing step, a preprocessing step)may be added elsewhere in the exemplary process/method 900. As anotherexample, all the steps in the exemplary process/method 900 may beimplemented in a computer-readable medium including a set ofinstructions. The instructions may be transmitted in a form ofelectronic current or electrical signals.

FIG. 10 is a flowchart of an exemplary process and/or method 1000 fordetermining a plurality of records according to some embodiments of thepresent disclose. In some embodiments, one or more steps in the process1000 may be implemented in the system 100 illustrated in FIG. 1. Forexample, one or more steps in the process 1000 may be stored in thestorage 130 and/or the storage (e.g., the ROM 230, the RAM 240, etc.) asa form of instructions, and invoked and/or executed by the server 110(e.g., the processing engine 112 in the server 110, or the processor 220of the processing engine 112 in the server 110).

In some embodiments, the processor 220 may implement the process and/ormethod 1000 for obtaining the plurality of records in the descriptionsof FIG. 5. For example, the processor 220 may implement the processand/or method 1000 for demining one or more spare-time records accordingto a decision-making processes model (e.g., a MDP model), so as toobtain the plurality of records. In some embodiments, the one or morespare-time records determined in the process and/or method 1000 may beused to determine a value function according to a reinforcement learningalgorithm in the descriptions of FIG. 5.

In 1010, the processor 220 (or the record determination module 420) mayidentify an order type of a subsequent order based on the orderinformation.

In some embodiments, the order type may refer to a kind of service thatis provided by the online on-demand platform. For example, the ordertype may include a taxi order, an express car order, a private carorder, a carpooling order, a bus order, a hitch order, a reservingorder, a real-time order, or the like, or any combination thereof. Insome embodiments, specific vehicles (and thus their associated drivers)can only handle certain order types. In some embodiments, some ordertypes are only available at specific time periods. In some embodiments,an order may belong to one or more order types (e.g., an order may beboth a carpooling order and a private car order). In some embodiments,the processor 220 may identify the order type based on the vehicleassociated with the subsequent order or the time information of thesubsequent order.

In some embodiments, the subsequent order may refer to an order startedat a most proximate time from an end time of a certain historical order(also referred to as the time finishing the certain historical order).For example, the end time of the certain historical order is 10:00, thesubsequent order may be an order that starts at a closest time pointafter 10:00, with no other orders in between.

In 1020, in response to identifying that the subsequent order as areserving order, the processor 220 (or the record determination module420) may determine a reference time point before a preset buffer timefrom a start time point of the reserving order.

In some embodiments, the processor 220 may obtain the start time pointof the reserving order from the order information. In some embodiments,the preset buffer time may be determined according to differentapplication scenarios. For example, the preset buffer time may bedifferent according to different areas, different start time of thereserving order, or the like, or any combination thereof. As anotherexample, the preset buffer time may be a certain period of timedetermined by the system 100. For example, the preset buffer time may be1 hour, 30 minutes, 10 minutes, 5 minutes, etc.

Before the start time point of the reserving order, the processor 220may determine the reference time point before the preset buffer timefrom the start time point of the reserving order. For example, thepreset buffer time is 30 minutes, the start time point of the reservingorder is 13:45, and the reference time point may be 13:15.

In 1030, the processor 220 (or the record determination module 420) maydetermine at least one spare-time record that corresponds to a period ofidle time between a time point for finishing the historical order andthe reference time point.

From the time point for finishing the historical order to the referencetime point, the processor 220 may generate a spare-time record everyperiod of idle time. For example, a historical driver finished ahistorical order at 10:00, the start time point of the reserving orderis 13:45, and the reference time point is 13:15. From 10:00 to 13:15,the processor 220 may generate a spare-time record every 10 minutes.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional steps (e.g., a storing step, a preprocessing step)may be added elsewhere in the exemplary process/method 1000. As anotherexample, all the steps in the exemplary process/method 1000 may beimplemented in a computer-readable medium including a set ofinstructions. The instructions may be transmitted in a form ofelectronic current or electrical signals.

FIG. 11 is a flowchart of an exemplary process and/or method 700 fordetermining a first matching value for each driver-order pair accordingto some embodiments of the present disclose. In some embodiments, one ormore steps in the process 1100 may be implemented in the system 100illustrated in FIG. 1. For example, one or more steps in the process1100 may be stored in the storage 130 and/or the storage (e.g., the ROM230, the RAM 240, etc.) as a form of instructions, and invoked and/orexecuted by the server 110 (e.g., the processing engine 112 in theserver 110, or the processor 220 of the processing engine 112 in theserver 110).

In some embodiments, the processor 220 may implement the process and/ormethod 1100 for obtaining the plurality of records in the descriptionsof FIG. 5. For example, the processor 220 may implement the processand/or method 1100 for demining one or more spare-time records accordingto a decision-making processes model (e.g., the MDP model), so as toobtain the plurality of records. In some embodiments, the one or morespare-time records determined in the process and/or method 1100 may beused to determine a value function according to a reinforcement learningalgorithm in the descriptions of FIG. 5.

In 1110, the processor 220 (or the record determination module 420) mayidentify a service type of the historical driver.

In some embodiments, the service type may refer to a kind of servicethat a driver may offer with his/her vehicle. In some embodiments, theservice type may be associated with the vehicle of the driver becausecertain vehicles are allowed to serve certain types of orders. Forexample, the service type of a historical driver may include a taxiservice, an express car service, a private car service, a bus service,or the like, or any combination thereof. In some embodiments, theprocessor 220 may identify the service type based on a vehicleinformation of the driver.

In 1120, the processor 220 (or the record determination module 420) mayidentify an order type of a subsequent order based on the orderinformation.

For example, the order type may include a taxi order, an express carorder, a private car order, a bus order, or the like, or any combinationthereof. In some embodiments, the processor 220 may identify the ordertype based on a vehicle associated with the subsequent order.

In some embodiments, the subsequent order may refer to an order startedat a most proximate time after an end time of a certain historical order(also refers to as the time finishing the certain historical order). Forexample, the end time of the certain historical order is 10:00, thesubsequent order may be an order that started at a closest time pointafter 10:00, with no other orders in between.

In 1130, in response to identifying that the subsequent order as anupgrade order for the historical driver, the processor 220 (or therecord determination module 420) may identify a start time point for thehistorical driver to receive a normal order associated with the sameservice type.

In some embodiments, the upgrade order may refer to an order thatmismatches with a driver. For example, the order type of the upgradeorder is different from the order type that the service type of driverprovides. As another example, when a private car driver receives (or isallocated to) an express car order (or a taxi order), the express carorder (or the taxi order) is an upgrade order for the private cardriver. In some embodiments, the processor 220 may identify the upgradeorder based on the order type of the subsequent order and the servicetype of the historical driver.

In some embodiments, the normal order associated with the same servicetype may refer to an order of a same order type with the service type ofthe historical driver. For example, the historical driver is a privatecar driver, the normal order may be a private car order.

In some embodiments, the processor 220 may identify the normal orderassociated with the same service type based on the order information andthe driver information. For example, the processor 220 may firstdetermine an area where the historical driver accepted (or was allocatedto) the upgrade order, and a time point that the historical driverfinished the historical order based on the order information and thecorresponding driver information. The processor 220 may obtain aplurality of normal orders of the same service type, and compare thestart time to receive the plurality of normal orders. The time pointthat is closest to the time point of finishing the historical order maybe identified as the start point for the historical driver to receivethe normal order.

In 1140, the processor 220 (or the record determination module 420) maydetermine at least one spare-time record that corresponds to a period ofidle time between finishing the historical order and the start timepoint.

From the time point for finishing the historical order to the start timepoint of the normal order, the processor 220 may generate a spare-timerecord in every period of idle time. For example, the processor 220 maygenerate a spare-time record every 10 minutes the time point forfinishing the historical order and the start time point of the normalorder.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore other optional steps (e.g., a storing step, a preprocessing step)may be added elsewhere in the exemplary process/method 1100. As anotherexample, all the steps in the exemplary process/method 1100 may beimplemented in a computer-readable medium including a set ofinstructions. The instructions may be transmitted in a form ofelectronic current or electrical signals.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by the present disclosure,and are within the spirit and scope of the exemplary embodiments of thepresent disclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment,” “one embodiment,” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 1703, Perl, COBOL1702, PHP, ABAP, dynamic programming languages such as Python, Ruby, andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a software as a service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software-only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

We claim:
 1. A system for determining and optimizing an allocationstrategy for an online on-demand transportation service, comprising: atleast one storage medium including a set of instructions for determininga value function in an online on-demand transportation service; and atleast one processor in communication with the storage medium, whereinwhen executing the set of instructions, the at least one processor isdirected to: store, into the at least one storage medium, a plurality ofhistorical orders collected from a plurality of passenger terminals anda plurality of driver terminals; obtain order information associatedwith the plurality of historical orders by accessing the at least onestorage medium; obtain driver information associated with a plurality ofhistorical drivers corresponding to the plurality of historical ordersby accessing the at least one storage medium; for each historical driverduring a predetermined period of time, determine a plurality of recordsbased on the order information and the driver information according to adecision-making processes model, wherein each record includes a driver'sspace-time status, a driver's action, a driver's revenue, and a driver'ssubsequent space-time status, and the decision-making processes model isa Markov Decision Process (MDP) model, wherein the order information andthe driver information are inputs of the MDP model, and the outputs ofthe MDP model are the plurality of records; determine a value functionbased on the plurality of records of each historical driver according toa reinforcement learning model, wherein the plurality of records of eachhistorical driver are inputs of the reinforcement learning model, andthe output of the reinforcement learning model is the value function;and utilize the value function to determine an allocation strategy forthe online on-demand transportation service.
 2. The system of claim 1,wherein the reinforcement learning model is a temporal-differencelearning model or a dynamic programming model.
 3. The system of claim 1,wherein for each historical driver during the predetermined period oftime, to determine the plurality of records, the at least one processoris further directed to: determine one or more order records, eachcorresponding to a historical order; and determine one or morespare-time records, each corresponding to a period of idle time notassociated with any historical order.
 4. The system of claim 1, whereinfor each historical driver during the predetermined period of time, todetermine the plurality of records, the at least one processor isfurther directed to: determine one or more order records, eachcorresponding to a historical order; obtain an online time point and anoffline time point of the historical driver in the predetermined periodof time based on the driver information; determine at least one ofspare-time records that corresponds to a period of idle time between theonline time point and a time point for accepting a first historicalorder; and determine at least another one of spare-time records thatcorresponds to a period of idle time between a time point for finishinga last historical order and the offline time point.
 5. The system ofclaim 1, wherein the plurality of records include at least one orderrecord and at least one spare-time record, wherein an order recordcorresponds to a historical order and includes at least one of: adriver's space-time status including a time and a location of thehistorical driver when accepting the historical order, a driver's actionincluding accepting the historical order, a driver's revenue including avalue of the historical order, or a driver's subsequent space-timestatus including a time and a location of the historical driver whenfinishing the historical order; and wherein a spare-time recordcorresponds to a period of idle time not associated with any historicalorder and includes at least one of: a driver's space-time statusincluding a time and a location of the historical driver during theperiod of idle time, a driver's action including being idle during theperiod of idle time, a driver's revenue including zero, or a driver'ssubsequent space-time status including a subsequent time and asubsequent location of the historical driver at the end of the period ofidle time, wherein the period of idle time is a predetermined durationin the idle time.
 6. The system of claim 5, wherein for each historicaldriver during the predetermined period of time, to determine theplurality of records, the at least one processor is further directed to:identify an order type of a subsequent order based on the orderinformation; in response to identifying that the subsequent order is areserving order, determine a reference time point before a preset buffertime from a start time point of the reserving order; and determine atleast one spare-time record that corresponds to a period of idle timebetween a time point for finishing the historical order and thereference time point.
 7. The system of claim 5, wherein for eachhistorical driver during the predetermined period of time, to determinethe plurality of records, the at least one processor is further directedto: identify a service type of the historical driver; identify an ordertype of a subsequent order based on the order information; in responseto identifying that the subsequent order is an upgrade order for thehistorical driver, identify a start time point for the historical driverto receive a normal order associated with the same service type; anddetermine at least one spare-time record that corresponds to a period ofidle time between finishing the historical order and the start timepoint.
 8. The system of claim 5, wherein for each historical driverduring the predetermined period of time, to determine the plurality ofrecords, the at least one processor is further directed to: determinethe order record that corresponds to the historical order, wherein thehistorical order is a general carpooling order that combines a pluralityof carpooling orders having a same route ID, the time and the locationof the historical driver are respectively a time and a location of thehistorical driver when first accepting the general carpooling order, andthe value of the historical order is a sum of values of the carpoolingorders.
 9. The system of claim 1, wherein when executing the set ofinstructions, the at least one processor is further directed to:optimize allocations of the drivers to the incoming orders based on thevalue function.
 10. A method for determining and optimizing anallocation strategy for an online on-demand transportation serviceimplemented on a computing device having at least one processor, atleast one storage medium, and a communication platform connected to anetwork, comprising: storing, into the at least one storage medium, aplurality of historical orders collected from a plurality of passengerterminals and a plurality of driver terminals; obtain order informationassociated with the plurality of historical orders by accessing the atleast one storage medium; obtaining driver information associated with aplurality of historical drivers corresponding to the plurality ofhistorical orders by accessing the at least one storage medium; for eachhistorical driver during a predetermined period of time, determining aplurality of records based on the order information and the driverinformation according to a decision-making processes model, wherein eachrecord includes a driver's space-time status, a driver's action, adriver's revenue, and a driver's subsequent space-time status, and thedecision-making processes model is a Markov Decision Process (MDP)model, wherein the order information and the driver information areinputs of the MDP model, and the outputs of the MDP model are theplurality of records; determining a value function based on theplurality of records of each historical driver according to areinforcement learning model, wherein the plurality of records of eachhistorical driver are inputs of the reinforcement learning model, andthe output of the reinforcement learning model is the value function;and utilizing the value function to determine an allocation strategy forthe online on-demand transportation service.
 11. The method of claim 10,wherein the reinforcement learning model is a temporal-differencelearning model or a dynamic programming model.
 12. The method of claim10, wherein for each historical driver during the predetermined periodof time, the determining the plurality of records includes: determiningone or more order records, each corresponding to a historical order; anddetermining one or more spare-time records, each corresponding to aperiod of idle time not associated with any historical order.
 13. Themethod of claim 10, wherein for each historical driver during thepredetermined period of time, the determining the plurality of recordsincludes: determining one or more order records, each corresponding to ahistorical order; obtaining an online time point and an offline timepoint of the historical driver in the predetermined period of time basedon the driver information; determining at least one of spare-timerecords that corresponds to a period of idle time between the onlinetime point and a time point for accepting a first historical order; anddetermining at least another one of spare-time records that correspondsto a period of idle time between a time point for finishing a lasthistorical order and the offline time point.
 14. The method of claim 10,wherein the plurality of records include at least one order record andat least one spare-time record, wherein an order record corresponds to ahistorical order and includes at least one of: a driver's space-timestatus including a time and a location of the historical driver whenaccepting the historical order, a driver's action including acceptingthe historical order, a driver's revenue including a value of thehistorical order, and a driver's subsequent space-time status includinga time and a location of the historical driver when finishing thehistorical order; or wherein a spare-time record corresponds to a periodof idle time not associated with any historical order and includes atleast one of: a driver's space-time status including a time and alocation of the historical driver during the period of idle time, adriver's action including being idle during the period of idle time, adriver's revenue including zero, or a driver's subsequent space-timestatus including a subsequent time and a subsequent location of thehistorical driver at the end of the period of idle time, wherein theperiod of idle time is a predetermined duration in the idle time. 15.The method of claim 14, wherein for each historical driver during thepredetermined period of time, the determining the plurality of recordsincludes: identifying an order type of a subsequent order based on theorder information; in response to identifying that the subsequent orderis a reserving order, determining a reference time point before a presetbuffer time from a start time point of the reserving order; anddetermining at least one spare-time record that corresponds to a periodof idle time between a time point for finishing the historical order andthe reference time point.
 16. The method of claim 14, wherein for eachhistorical driver during the predetermined period of time, thedetermining the plurality of records includes: identifying a servicetype of the historical driver; identifying an order type of a subsequentorder based on the order information; in response to identifying thatthe subsequent order is an upgrade order for the historical driver,identifying a start time point for the historical driver to receive anormal order associated with the same service type; and determining atleast one spare-time record that corresponds to a period of idle timebetween finishing the historical order and the start time point.
 17. Themethod of claim 14, wherein for each historical driver during thepredetermined period of time, the determining the plurality of recordsincludes: determining the order record that corresponds to thehistorical order, wherein the historical order is a general carpoolingorder that combines a plurality of carpooling order having a same routeID, the time and the location of the historical driver are respectivelya time and a location of the historical drive when first accepting thegeneral carpooling order, and the value of the historical order is a sumof values of the carpooling orders.
 18. A non-transitory computerreadable medium, comprising at least one set of instructions fordetermining and optimizing an allocation strategy for an onlineon-demand transportation service, wherein when executing the set ofinstructions, the at least one set of instructions directs the at leastone processor to: store, into at least one storage medium, a pluralityof historical orders collected from a plurality of passenger terminalsand a plurality of driver terminals; obtain order information associatedwith the plurality of historical orders by accessing the at least onestorage medium; obtain driver information associated with a plurality ofhistorical drivers corresponding to the plurality of historical ordersby accessing the at least one storage medium; for each historical driverduring a predetermined period of time, determine a plurality of recordsbased on the order information and the driver information according to adecision-making processes model, wherein each record includes a driver'sspace-time status, a driver's action, a driver's revenue, and a driver'ssubsequent space-time status, and the decision-making processes model isa Markov Decision Process (MDP) model, wherein the order information andthe driver information are inputs of the MDP model, and the outputs ofthe MDP model are the plurality of records; determine a value functionbased on the plurality of records of each historical driver according toa reinforcement learning model, wherein the plurality of records of eachhistorical driver are inputs of the reinforcement learning model, andthe output of the reinforcement learning model is the value function;and utilize the value function to determine an allocation strategy forthe online on-demand transportation service.